
import cosima_cookbook as cc
from cosima_cookbook import explore
import numpy as np
import dask.array as da
import xarray as xr
import pandas as pd
import os.path
import glob
from tqdm.notebook import tqdm

#------------------------

#----------------------
import logging
logging.captureWarnings(True)
logging.getLogger('py.warnings').setLevel(logging.ERROR)

from dask.distributed import Client

# change to your own directory on /g/data/:
figdir = '/g/data/hh5/tmp/rsp599_tmp/ACESSOM201/figures/BGC_IAF/'


%matplotlib inline
%config InlineBackend.figure_format='retina'

#Packages for plotting
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import matplotlib.colors as mcolors
from matplotlib.ticker import AutoMinorLocator
import matplotlib.cm as mcm
import matplotlib.gridspec as gridspec
#
import IPython.display
import cmocean as cm           
import cartopy.crs as ccrs
import cartopy.feature as cft
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import geopandas
#
land_50m = cft.NaturalEarthFeature('physical', 'land', '50m',
                                   edgecolor='black', facecolor='gray', linewidth=0.5)


client = Client()
client
# client = Client(processes=2, threads_per_worker=1,
#                 n_workers=2, memory_limit='10GB')
# client


! ls /g/data/ik11/observations/woa18_bgc/woa18_bgc_10/

woa18_all_n01_10.nc  woa18_all_o01_10.nc  woa18_all_p01_10.nc
woa18_all_n02_10.nc  woa18_all_o02_10.nc  woa18_all_p02_10.nc
woa18_all_n03_10.nc  woa18_all_o03_10.nc  woa18_all_p03_10.nc
woa18_all_n04_10.nc  woa18_all_o04_10.nc  woa18_all_p04_10.nc
woa18_all_n05_10.nc  woa18_all_o05_10.nc  woa18_all_p05_10.nc
woa18_all_n06_10.nc  woa18_all_o06_10.nc  woa18_all_p06_10.nc
woa18_all_n07_10.nc  woa18_all_o07_10.nc  woa18_all_p07_10.nc
woa18_all_n08_10.nc  woa18_all_o08_10.nc  woa18_all_p08_10.nc
woa18_all_n09_10.nc  woa18_all_o09_10.nc  woa18_all_p09_10.nc
woa18_all_n10_10.nc  woa18_all_o10_10.nc  woa18_all_p10_10.nc
woa18_all_n11_10.nc  woa18_all_o11_10.nc  woa18_all_p11_10.nc
woa18_all_n12_10.nc  woa18_all_o12_10.nc  woa18_all_p12_10.nc


!module load nco
!ncrcat /g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_o*_10.nc /g/data/hh5/tmp/rsp599_tmp/nitrate.nc

ncrcat: ERROR no variables fit criteria for processing
ncrcat: HINT Extraction list must contain a record variable to concatenate. A record variable is a variable defined with a record dimension. Often the record dimension, aka unlimited dimension, refers to time. To change an existing dimension from a fixed to a record dimensions see http://nco.sf.net/nco.html#mk_rec_dmn or to add a new record dimension to all variables see http://nco.sf.net/nco.html#ncecat_rnm


!ncdump -h /g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n01_10.nc

netcdf woa18_all_n01_10 {
dimensions:
	xt_ocean = 3600 ;
	yt_ocean = 2700 ;
	st_ocean = 44 ;
	time = 1 ;
variables:
	double xt_ocean(xt_ocean) ;
		xt_ocean:_FillValue = NaN ;
		xt_ocean:long_name = "Nominal Longitude of T-cell center" ;
		xt_ocean:units = "degree_east" ;
		xt_ocean:modulo = 360. ;
		xt_ocean:point_spacing = "even" ;
		xt_ocean:axis = "X" ;
	double yt_ocean(yt_ocean) ;
		yt_ocean:_FillValue = NaN ;
		yt_ocean:long_name = "tcell latitude" ;
		yt_ocean:units = "degrees_N" ;
		yt_ocean:cartesian_axis = "Y" ;
	double st_ocean(st_ocean) ;
		st_ocean:_FillValue = NaN ;
		st_ocean:long_name = "zt" ;
		st_ocean:units = "meters" ;
		st_ocean:positive = "down" ;
		st_ocean:point_spacing = "uneven" ;
		st_ocean:axis = "Z" ;
	double time(time) ;
		time:_FillValue = NaN ;
		time:long_name = "time" ;
		time:cartesian_axis = "T" ;
		time:calendar_type = "GREGORIAN" ;
		time:units = "days since 0001-01-01" ;
		time:calendar = "GREGORIAN" ;
	double n_an(time, st_ocean, yt_ocean, xt_ocean) ;
		n_an:_FillValue = NaN ;
		n_an:long_name = "Objectively analyzed mean fields for moles_concentration_of_nitrate_in_sea_water at standard depth levels." ;
		n_an:units = "micromoles_per_kilogram" ;
}


no3 = xr.open_mfdataset('/g/data/hh5/tmp/rsp599_tmp/nitrate_srf_mon_clim_2000_2019.nc')
no3

<xarray.Dataset>
Dimensions:   (xt_ocean: 3600, yt_ocean: 2700, month: 12)
Coordinates:
  * xt_ocean  (xt_ocean) float64 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0
  * yt_ocean  (yt_ocean) float64 -81.11 -81.07 -81.02 ... 89.89 89.94 89.98
  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
Data variables:
    Nitrate   (month, yt_ocean, xt_ocean) float32 dask.array<chunksize=(12, 2700, 3600), meta=np.ndarray>
Attributes:
    long_name:                                          nitrate
    units:                                              mmol/m^3
    valid_range:                                        [-1000000.  1000000.]
    cell_methods:                                       time: mean
    time_avg_info:                                      average_T1,average_T2...
    QuantizeGranularBitRoundNumberOfSignificantDigits:  3
    ncfiles:                                            ['/g/data/cj50/access...
    contact:                                            Andrew Kiss
    email:                                              andrew.kiss@anu.edu.au
    created:                                            2022-04-27
    description:                                        0.1 degree ACCESS-OM2...
    notes:                                              Run configuration and...

array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95])

array([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,
        89.978896])

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

PandasIndex(Float64Index([ -179.9499999999989,  -179.8499999999989, -179.74999999999892,
               -179.6499999999989, -179.54999999999893,  -179.4499999999989,
              -179.34999999999894, -179.24999999999892, -179.14999999999895,
              -179.04999999999893,
              ...
               179.05000000000115,  179.15000000000117,  179.25000000000114,
               179.35000000000116,  179.45000000000113,  179.55000000000115,
                179.6500000000011,  179.75000000000114,   179.8500000000011,
               179.95000000000113],
             dtype='float64', name='xt_ocean', length=3600))

PandasIndex(Float64Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,
              -80.98191361521337, -80.93967426083289,  -80.8974349064524,
              -80.85519555207192, -80.81295619769143, -80.77071684331095,
              -80.72847748893047,
              ...
               89.59874190883218,   89.6409812632127,   89.6832206175932,
               89.72545997197363,  89.76769932635409,  89.80993868073463,
               89.85217803511516,  89.89441738949557,    89.936656743876,
               89.97889609825653],
             dtype='float64', name='yt_ocean', length=2700))

PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n01_10.nc')
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)

<xarray.DataArray 'n_an' (st_ocean: 44, yt_ocean: 2700, xt_ocean: 3600)>
dask.array<getitem, shape=(44, 2700, 3600), dtype=float64, chunksize=(44, 2700, 3600), chunktype=numpy.ndarray>
Coordinates:
  * xt_ocean  (xt_ocean) float64 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0
  * yt_ocean  (yt_ocean) float64 -81.11 -81.07 -81.02 ... 89.89 89.94 89.98
  * st_ocean  (st_ocean) float64 0.5413 1.681 2.94 4.332 ... 630.7 695.4 766.1
Attributes:
    long_name:  Objectively analyzed mean fields for moles_concentration_of_n...
    units:      micromoles_per_kilogram

array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95])

array([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,
        89.978896])

array([5.412807e-01, 1.680735e+00, 2.939953e+00, 4.331522e+00, 5.869350e+00,
       7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,
       1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,
       3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,
       6.969342e+01, 7.809601e+01, 8.737988e+01, 9.763699e+01, 1.089687e+02,
       1.214869e+02, 1.353144e+02, 1.505868e+02, 1.674530e+02, 1.860765e+02,
       2.066365e+02, 2.293296e+02, 2.543701e+02, 2.819920e+02, 3.124492e+02,
       3.460166e+02, 3.829906e+02, 4.236883e+02, 4.684475e+02, 5.176241e+02,
       5.715899e+02, 6.307275e+02, 6.954248e+02, 7.660668e+02])

PandasIndex(Float64Index([ -179.9499999999989,  -179.8499999999989, -179.74999999999892,
               -179.6499999999989, -179.54999999999893,  -179.4499999999989,
              -179.34999999999894, -179.24999999999892, -179.14999999999895,
              -179.04999999999893,
              ...
               179.05000000000115,  179.15000000000117,  179.25000000000114,
               179.35000000000116,  179.45000000000113,  179.55000000000115,
                179.6500000000011,  179.75000000000114,   179.8500000000011,
               179.95000000000113],
             dtype='float64', name='xt_ocean', length=3600))

PandasIndex(Float64Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,
              -80.98191361521337, -80.93967426083289,  -80.8974349064524,
              -80.85519555207192, -80.81295619769143, -80.77071684331095,
              -80.72847748893047,
              ...
               89.59874190883218,   89.6409812632127,   89.6832206175932,
               89.72545997197363,  89.76769932635409,  89.80993868073463,
               89.85217803511516,  89.89441738949557,    89.936656743876,
               89.97889609825653],
             dtype='float64', name='yt_ocean', length=2700))

PandasIndex(Float64Index([0.5412807464599609,  1.680734634399414, 2.9399526119232178,
               4.331521511077881,  5.869350433349609,  7.568809986114502,
               9.446885108947754, 11.522344589233398, 13.815927505493164,
              16.350553512573242, 19.151540756225586, 22.246871948242188,
               25.66745948791504,  29.44745635986328,  33.62459945678711,
               38.24056625366211,  43.34140396118164, 48.977962493896484,
               55.20640182495117,  62.08873748779297,  69.69342041015625,
               78.09600830078125,      87.3798828125,  97.63699340820312,
              108.96874237060547, 121.48686218261719, 135.31442260742188,
               150.5868377685547,  167.4530029296875,  186.0764617919922,
              206.63653564453125, 229.32957458496094, 254.37013244628906,
               281.9919738769531, 312.44915771484375,  346.0166320800781,
               382.9905700683594,  423.6883239746094,   468.447509765625,
               517.6241455078125,  571.5899047851562,     630.7275390625,
                  695.4248046875,  766.0667724609375],
             dtype='float64', name='st_ocean'))


# fig, axes = plt.subplots(ncols = 1, nrows=2, subplot_kw={'projection': ccrs.Robinson()}, figsize = (16, 6))

# cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
#                'fraction': 0.03,
#                'aspect': 15,
#                'shrink': 0.7}

# no3.Nitrate.isel(month=1).plot(ax=axes[0],
#                                x='xt_ocean', y='yt_ocean',
#                                transform=ccrs.PlateCarree(),
#                                vmin=0, vmax=30, extend='both',
#                                cmap=cm.cm.matter, cbar_kwargs=cbar_kwargs)

# woa_no3.n_an.isel(st_ocean=0).plot(ax=axes[1],
#                                    x='xt_ocean', y='yt_ocean',
#                                    transform=ccrs.PlateCarree(),
#                                    vmin=0, vmax=30, extend='both',
#                                    cmap=cm.cm.matter,
#                                    cbar_kwargs=cbar_kwargs)


# axes[0].set_title('January Climatology ACCESS-OM')
# axes[1].set_title('January Climatology WOA18');

# import string

# # add features and label panels
# for n, ax in enumerate(axes):
#     # features
#     ax.coastlines(resolution='50m')
#     ax.add_feature(land_50m)
#     ax.gridlines(draw_labels=False)
    
#     # subplot labels
#     ax.text(0, 0.9, '('+string.ascii_lowercase[n]+')', transform=ax.transAxes, size=16)


no3diff = no3.Nitrate.isel(month=0) - woa_no3.n_an.isel(st_ocean=0)
no3diff.load()

<xarray.DataArray (yt_ocean: 2700, xt_ocean: 3600, time: 1)>
array([[[       nan],
        [       nan],
        [       nan],
        ...,
        [       nan],
        [       nan],
        [       nan]],

       [[       nan],
        [       nan],
        [       nan],
        ...,
        [       nan],
        [       nan],
        [       nan]],

       [[       nan],
        [       nan],
        [       nan],
        ...,
...
        ...,
        [8.46696397],
        [8.48541251],
        [8.50233707]],

       [[8.52319097],
        [8.53394496],
        [8.54389056],
        ...,
        [8.47821153],
        [8.49511876],
        [8.51007693]],

       [[8.51721955],
        [8.52985752],
        [8.54229768],
        ...,
        [8.47746348],
        [8.49057766],
        [8.50500556]]])
Coordinates:
  * xt_ocean  (xt_ocean) float64 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0
  * yt_ocean  (yt_ocean) float64 -81.11 -81.07 -81.02 ... 89.89 89.94 89.98
    month     int64 1
    st_ocean  float64 0.5413
  * time      (time) object 0001-01-02 00:00:00

array([[[       nan],
        [       nan],
        [       nan],
        ...,
        [       nan],
        [       nan],
        [       nan]],

       [[       nan],
        [       nan],
        [       nan],
        ...,
        [       nan],
        [       nan],
        [       nan]],

       [[       nan],
        [       nan],
        [       nan],
        ...,
...
        ...,
        [8.46696397],
        [8.48541251],
        [8.50233707]],

       [[8.52319097],
        [8.53394496],
        [8.54389056],
        ...,
        [8.47821153],
        [8.49511876],
        [8.51007693]],

       [[8.51721955],
        [8.52985752],
        [8.54229768],
        ...,
        [8.47746348],
        [8.49057766],
        [8.50500556]]])

array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95])

array([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,
        89.978896])

array(1)

array(0.54128075)

array([cftime.DatetimeGregorian(1, 1, 2, 0, 0, 0, 0, has_year_zero=False)],
      dtype=object)

PandasIndex(Float64Index([ -179.9499999999989,  -179.8499999999989, -179.74999999999892,
               -179.6499999999989, -179.54999999999893,  -179.4499999999989,
              -179.34999999999894, -179.24999999999892, -179.14999999999895,
              -179.04999999999893,
              ...
               179.05000000000115,  179.15000000000117,  179.25000000000114,
               179.35000000000116,  179.45000000000113,  179.55000000000115,
                179.6500000000011,  179.75000000000114,   179.8500000000011,
               179.95000000000113],
             dtype='float64', name='xt_ocean', length=3600))

PandasIndex(Float64Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,
              -80.98191361521337, -80.93967426083289,  -80.8974349064524,
              -80.85519555207192, -80.81295619769143, -80.77071684331095,
              -80.72847748893047,
              ...
               89.59874190883218,   89.6409812632127,   89.6832206175932,
               89.72545997197363,  89.76769932635409,  89.80993868073463,
               89.85217803511516,  89.89441738949557,    89.936656743876,
               89.97889609825653],
             dtype='float64', name='yt_ocean', length=2700))

PandasIndex(CFTimeIndex([0001-01-02 00:00:00],
            dtype='object', length=1, calendar='standard', freq=None))


fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Jan Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n02_10.nc')
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=1) - woa_no3.n_an.isel(st_ocean=0)
no3diff.load()
#
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Feb Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n03_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=2) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('March Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n04_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=3) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('April Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n05_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=4) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('May Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n06_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=5) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('June Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n07_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=6) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('July Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n08_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=7) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Aug Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n09_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=8) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Sep Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n10_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=9) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Oct Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n11_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=10) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Nov Clim (ACCES - WOA)');


woa_no3 = xr.open_mfdataset('/g/data/ik11/observations/woa18_bgc/woa18_bgc_10/woa18_all_n12_10.nc') # change filename
woa_no3 = woa_no3.assign_coords(xt_ocean=(((woa_no3.xt_ocean + 180) % 360) - 180)).sortby('xt_ocean')
woa_no3.n_an.squeeze(drop=True)
#
no3diff = no3.Nitrate.isel(month=11) - woa_no3.n_an.isel(st_ocean=0)### Change month name
no3diff.load()
# plotting
fig, axes = plt.subplots(ncols = 1, nrows=1, subplot_kw={'projection': ccrs.Robinson()}, figsize = (10, 6))

cbar_kwargs = {'label': '$NO_3 (mmolm^{-3}$)',
               'fraction': 0.03,
               'aspect': 15,
               'shrink': 0.7}

axes.coastlines(resolution='50m')
axes.add_feature(land_50m)
axes.gridlines(draw_labels=False)

no3diff.plot(ax=axes,
             x='xt_ocean', y='yt_ocean',
             transform=ccrs.PlateCarree(),
             robust=True, extend='both',
             cmap=cm.cm.diff, cbar_kwargs=cbar_kwargs)

plt.title('Dec Clim (ACCES - WOA)');

Dashboard: /proxy/42597/status	Workers: 4
Total threads: 16	Total memory: 44.92 GiB
Status: running	Using processes: True

Comm: tcp://127.0.0.1:43713	Workers: 4
Dashboard: /proxy/42597/status	Total threads: 16
Started: Just now	Total memory: 44.92 GiB

Comm: tcp://127.0.0.1:37021	Total threads: 4
Dashboard: /proxy/44653/status	Memory: 11.23 GiB
Nanny: tcp://127.0.0.1:45285
Local directory: /local/p93/rsp599/tmp/dask-worker-space/worker-w2qbeq7a

Comm: tcp://127.0.0.1:43719	Total threads: 4
Dashboard: /proxy/34653/status	Memory: 11.23 GiB
Nanny: tcp://127.0.0.1:44203
Local directory: /local/p93/rsp599/tmp/dask-worker-space/worker-5_jv0ml3

Comm: tcp://127.0.0.1:39265	Total threads: 4
Dashboard: /proxy/36927/status	Memory: 11.23 GiB
Nanny: tcp://127.0.0.1:39013
Local directory: /local/p93/rsp599/tmp/dask-worker-space/worker-2rsbf9wf

Evaluation of surface nitrate concentration in ACCESS0M2-01¶

Client

Cluster Info

LocalCluster

Scheduler Info

Scheduler

Workers

Worker: 0

Worker: 1

Worker: 2

Worker: 3

Evaluating against WOA¶

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: /proxy/42597/status

Comm: tcp://127.0.0.1:36493	Total threads: 4
Dashboard: /proxy/40415/status	Memory: 11.23 GiB
Nanny: tcp://127.0.0.1:44141
Local directory: /local/p93/rsp599/tmp/dask-worker-space/worker-5uyyp3ev